A dictionary learning algorithm for multi-channel neural recordings

Multi-channel neural recording devices are widely used for in vivo neuroscience experiments. Incurred by high signal frequency and large channel numbers, the acquisition rate could be on the order of hundred MB/s, which requires compression before wireless transmission. In this paper, we adopt the Compressed Sensing framework with a simple on-chip implementation. To improve the performance while reducing the number of measurements, we propose a multi-modal structured dictionary learning algorithm that enforces both group sparsity and joint sparsity to learn sparsifying dictionaries for all channels simultaneously. When the data is compressed 50 times, our method can achieve a gain of 4 dB and 10 percentage units over state-of-art approaches in terms of the reconstruction quality and classification accuracy, respectively.